Geospatial Data for Solar Site Selection in Italy

Why geospatial data decides solar economics

Two adjacent rooftops in the same municipality can have wildly different payback periods. Irradiance, tilt, shading, grid proximity, land constraints, ownership and local market prices all change within a few hundred metres. Static dashboards and national averages flatten this away. Geospatial data — used properly — preserves it.

In Italy, the raw material is unusually good: most of what you need for serious site selection is already public. The hard part is connecting it.

The core Italian open datasets

• PVGIS (JRC) — modelled solar irradiance and PV yield anywhere in Europe. The baseline for "how much sun does this roof actually get".
• Copernicus HRL — high-resolution land cover, imperviousness, tree-cover density. Tells you whether the ground is buildable and whether a roof is shaded by canopy.
• National building footprints — 18.3M polygons covering Italy's built stock. Without this layer "per-building economics" is impossible.
• Agenzia delle Entrate (OMI) — official real-estate market values per microzone. Anchors property-level investment logic to a defensible price baseline.
• ISTAT — population, households, energy consumption proxies at municipality level. Used to size local demand and CER (renewable energy community) scenarios.
• ISPRA — hydrogeological and landslide risk layers. The fastest way to drop sites that look great until you check exposure.
• OpenStreetMap — road network, substations, land use. Cheap-and-fast proxy for grid and access proximity.

A four-step screening workflow

1. Normalise to the building

Join PVGIS yield, Copernicus land/canopy class, and OMI microzone prices onto each building footprint. The building becomes the unit of analysis — not the cadastral parcel, not the municipality.

2. Score solar opportunity transparently

Combine irradiance, usable roof or land area, and grid proximity into a Solar Opportunity Score. Keep the weights visible. A black-box score that ranks a site #1 without explaining why is useless when a permitting authority asks.

3. Compute per-site economics

Payback, 20-year NPV, LCOE, and the 50% tax-deduction scenario where applicable. Layer in renewable-energy-community (CER) configurations using ISTAT-derived local demand. This is where geospatial data stops being a map and starts being a financial model.

4. Subtract risk

Overlay ISPRA hydrogeological and seismic layers. Sites in high-risk zones either drop out or get a higher cost-of-capital applied. Opportunity and exposure belong in the same decision surface, not in separate reports nobody opens together.

Common pitfalls

• Working at municipality level. Averages hide the buildings worth developing.
• Using irradiance alone. A south-facing roof on an unbuildable parcel scores high and goes nowhere.
• Ignoring OMI prices. Solar yield without an economic anchor produces rankings no investor will sign off on.
• Treating risk as a separate report. By the time it's a separate PDF, the decision is already emotionally made.

How Dotlink applies this

Dotlink's solar lens is exactly this pipeline, productised: 15+ official Italian datasets connected onto 18.3M building footprints, scored transparently, with per-building economics and CER scenarios — and risk layers in the same view. See Territorial Intelligence for the live application, or Our Approach for the method behind it.